Apparatus for implementing a data processing pipeline for machine condition monitoring and other applications is provided. The apparatus comprises data processing modules communicatively coupled in series, including plug-in modules configured to receive input data, and produce output data, at least some of which is used by at least one downstream improvement system to carry out remedial actions. The apparatus also comprises a data access layer configured to receive data and make it available in a unified data format to downstream data processing modules and the at least one downstream improvement system. The data access layer comprises an enterprise service bus, and a data unification processor to convert the input data to unified data objects and make these accessible to the plug-in modules and the at least one downstream improvement system via the bus.
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2. The apparatus of claim 1 wherein the data access layer comprises a data unification processor configured to convert the input data to a plurality of unified data objects and make the unified data objects accessible to the plurality of plug-in processing modules and the at least one downstream improvement system via the enterprise service bus.
This invention relates to a data processing apparatus designed to integrate and unify disparate data sources for use in enterprise systems. The apparatus addresses the challenge of managing and processing heterogeneous data from multiple sources, ensuring consistent access and compatibility across different enterprise applications and improvement systems. The apparatus includes a data access layer that interfaces with various data sources, such as databases, APIs, or other enterprise systems. Within this layer, a data unification processor converts input data from these sources into a standardized format, generating unified data objects. These objects are structured to ensure compatibility and interoperability across the system. The unified data objects are then made accessible to multiple plug-in processing modules, which can perform specialized data transformations, analytics, or other processing tasks. Additionally, the unified data objects are accessible to downstream improvement systems, such as machine learning models, business intelligence tools, or other enterprise applications, via an enterprise service bus (ESB). The ESB facilitates seamless communication and data exchange between the processing modules and the improvement systems, ensuring efficient and scalable data flow. The invention enables enterprises to consolidate and standardize data from diverse sources, improving data consistency, accessibility, and usability across the organization. This unified approach enhances the efficiency of data-driven decision-making and supports advanced analytics and automation workflows.
3. The apparatus of claim 2 wherein the data access layer comprises one or more filtering components configured to selectively make at least a portion of the input data available for conversion to unified data objects by the data unification processor.
This invention relates to data processing systems, specifically apparatuses for managing and transforming input data into unified data objects. The problem addressed is the challenge of integrating diverse data sources with varying formats, structures, and access requirements into a consistent, unified representation for downstream processing or analysis. The apparatus includes a data access layer that interfaces with multiple input data sources, which may include databases, files, or streaming data. The data access layer contains one or more filtering components that selectively expose portions of the input data for further processing. These filtering components can apply criteria such as data type, relevance, or access permissions to determine which data should be made available. The filtered data is then passed to a data unification processor, which converts the input data into unified data objects. These unified objects standardize the data into a common format, structure, and schema, enabling consistent processing and analysis across different data sources. The apparatus may also include a data storage layer to store the unified data objects for later retrieval or further processing. The filtering components ensure that only relevant or authorized data is processed, improving efficiency and security. The system is designed to handle large-scale data integration tasks while maintaining flexibility and adaptability to different data sources.
4. The apparatus of claim 1 wherein the data access layer comprises an output moderator configured to aggregate output data from the plurality of plug-in processing modules, and implement a conflict resolution process if a conflict is detected in the aggregate output data.
This invention relates to a data processing apparatus designed to handle conflicts in output data generated by multiple plug-in processing modules. The apparatus includes a data access layer that interfaces with these modules, which are independently developed and may produce conflicting results. The output moderator within the data access layer aggregates the output data from these modules and resolves conflicts when they arise. Conflict resolution ensures that the final output is consistent and reliable, even when different modules provide contradictory or overlapping data. The moderator may use predefined rules, priority schemes, or other conflict resolution techniques to determine the correct output. This system is particularly useful in environments where multiple data sources or processing modules must work together seamlessly, such as in enterprise software, data integration systems, or distributed computing frameworks. The invention improves data consistency and reliability by automatically detecting and resolving conflicts at the output stage, reducing the need for manual intervention or post-processing.
5. The apparatus of claim 4 wherein the data access layer is configured to receive baseline reference data comprising baseline intelligence and/or configuration data, the baseline reference data made available to the plurality of plug-in processing modules via the enterprise service bus.
This invention relates to an apparatus for managing data access and processing in a distributed computing environment. The apparatus addresses the challenge of integrating diverse data sources and processing modules within an enterprise system, ensuring efficient data flow and interoperability. The apparatus includes a data access layer that interfaces with multiple data sources, such as databases or external systems, to retrieve and manage data. This layer is designed to handle baseline reference data, which includes intelligence and configuration data, and makes this data available to a plurality of plug-in processing modules. The processing modules are dynamically loaded and executed to perform specific tasks, such as data transformation, analysis, or reporting. The apparatus also includes an enterprise service bus (ESB) that facilitates communication between the data access layer and the processing modules, ensuring seamless data exchange and coordination. The ESB acts as a central hub, routing messages and enabling the modules to access the baseline reference data as needed. This architecture allows for modular, scalable, and flexible data processing, accommodating varying enterprise requirements and reducing integration complexity. The invention improves system efficiency by standardizing data access and enabling real-time processing capabilities.
6. The apparatus of claim 5 wherein the output moderator is configured to adjust the baseline reference data to reduce the likelihood of conflict in output data from the plurality of plug-in processing modules.
This invention relates to an apparatus for managing output data from multiple plug-in processing modules in a system. The problem addressed is the potential for conflicts or inconsistencies in the output data generated by these modules when they operate independently. The apparatus includes an output moderator that adjusts baseline reference data to minimize such conflicts. The baseline reference data serves as a standardized framework or set of parameters that the plug-in modules use to process input data. By modifying this baseline data, the moderator ensures that the outputs from different modules align more closely, reducing discrepancies that could arise from variations in how each module interprets or processes the input. The moderator may apply rules, thresholds, or normalization techniques to the baseline data to achieve this alignment. The apparatus may also include a data interface for receiving input data and distributing it to the plug-in modules, as well as a controller for coordinating the processing tasks. The plug-in modules themselves are interchangeable components that perform specific data processing functions, such as filtering, transformation, or analysis. The overall system is designed to improve the reliability and consistency of the combined output data, making it suitable for applications where multiple processing modules must work together seamlessly.
7. The apparatus of claim 4 wherein the output moderator is configured to aggregate output data into hierarchies to produce higher level outputs, such as by roll-up of condition indicators across multiple asset components.
This invention relates to an apparatus for monitoring and analyzing asset conditions, particularly in industrial or infrastructure systems where multiple components contribute to overall system health. The apparatus addresses the challenge of managing large volumes of condition data from diverse asset components, which can be difficult to interpret without meaningful aggregation. The apparatus includes an output moderator that processes raw condition data from sensors or other monitoring devices attached to asset components. The moderator is configured to aggregate this data into hierarchical structures, enabling the generation of higher-level outputs. For example, it can perform roll-up operations to combine condition indicators from multiple components, providing a consolidated view of system health. This hierarchical aggregation allows users to monitor both individual component conditions and broader system performance trends. The apparatus may also include data processing modules to filter, normalize, or correlate the condition data before aggregation. The hierarchical outputs can be displayed or transmitted for further analysis, supporting predictive maintenance or operational decision-making. The invention improves situational awareness by transforming raw sensor data into structured, actionable insights.
8. The apparatus of claim 1 wherein the data access layer comprises a notification generator configured to generate a notification based at least in part on output data objects that are pushed onto the enterprise service bus which satisfy certain conditions.
This invention relates to data processing systems, specifically an apparatus for managing data access and notifications within an enterprise service bus (ESB) environment. The problem addressed is the need for automated monitoring and alerting when specific data conditions are met in a distributed system. The apparatus includes a data access layer that interfaces with an enterprise service bus (ESB) to process data objects. The data access layer contains a notification generator that monitors output data objects pushed onto the ESB. When these data objects meet predefined conditions, the notification generator triggers the creation of a notification. The conditions may include data values, thresholds, or other criteria relevant to enterprise operations. The notification can be sent to users, systems, or other components within the enterprise infrastructure to ensure timely awareness of significant events or anomalies. The apparatus may also include a data processing layer that prepares and formats data objects before they are pushed onto the ESB. This ensures that the data is in a standardized format suitable for further processing or analysis. The notification generator operates independently, allowing it to detect and respond to conditions without requiring additional manual intervention. This solution improves operational efficiency by automating the detection and reporting of critical data events, reducing the need for constant manual monitoring. The system is particularly useful in large-scale enterprise environments where real-time data processing and alerting are essential.
9. The apparatus of claim 1 wherein the plurality of plug-in processing modules comprises a data shaping module configured to process raw data and generate condition and key performance indicators based on the raw data, wherein each of the condition and key performance indicators comprises one or more of: a metric of a condition of an asset or process, attributes such as source data, processing descriptions and statistics, and audit information that can be used by the next step to assist in its subsequent use and interrogation.
This invention relates to an apparatus for processing and analyzing raw data from assets or processes to generate condition and key performance indicators (KPIs). The apparatus includes a plurality of plug-in processing modules, one of which is a data shaping module. The data shaping module processes raw data to produce condition and KPIs, each containing metrics that describe the state of an asset or process. These indicators also include attributes such as source data, processing descriptions, statistics, and audit information. The additional attributes ensure that the generated indicators can be effectively used and interrogated in subsequent processing steps. The modular design allows for flexible integration of different processing functions, enabling customization based on specific data analysis requirements. The apparatus is designed to enhance data usability by structuring raw data into meaningful indicators while preserving contextual information for further analysis. This approach improves decision-making by providing standardized, interpretable metrics alongside their processing history and metadata. The system is particularly useful in industrial or operational environments where real-time monitoring and performance tracking are critical.
10. The apparatus of claim 9 wherein the plurality of plug-in processing modules comprises a diagnosis/prognosis module configured to receive condition and key performance indicators generated by the data shaping module, and based at least in part on the condition and key performance indicators, generate fault objects, each of the fault objects comprising one or more of: a description or identification of the likely fault, confidence factor, weighted associated root cause, residual life estimate, meta-data about the method and explanation of how the conclusion was reached and a trace back to the underlying condition indications.
This invention relates to a diagnostic and prognostic system for industrial equipment, addressing the need for automated fault detection, root cause analysis, and predictive maintenance. The system includes a data shaping module that processes raw sensor data from equipment to generate condition indicators and key performance indicators (KPIs). These indicators are then analyzed by a diagnosis/prognosis module, which identifies potential faults and predicts equipment health. The diagnosis/prognosis module generates fault objects containing detailed information about detected faults, including a description or identification of the likely fault, a confidence factor indicating the likelihood of the fault, a weighted root cause analysis, an estimate of the equipment's remaining useful life, metadata about the diagnostic method, and an explanation of how the conclusion was reached. Additionally, the fault objects provide traceability back to the underlying condition indicators that triggered the diagnosis. The system is modular, allowing for plug-in processing modules to be added or removed as needed. This flexibility enables customization for different types of equipment or specific diagnostic requirements. The overall goal is to improve maintenance efficiency by providing actionable insights into equipment health, reducing downtime, and optimizing maintenance schedules.
11. The apparatus of claim 10 wherein the plurality of plug-in processing modules comprises a remedial action recommendation module configured to receive fault objects generated by the diagnosis/prognosis module, and based at least in part on the fault objects, generate remedial action objects, each of the remedial action objects comprising one or more of: a description or identification of the action that should to be taken to solve or prevent an issue, a priority and time-window within which the action should take place, an explanation tracing from data to condition insights to fault and root case, and domain specific properties for use by the at least one downstream improvement system.
This invention relates to a diagnostic and prognostic system for industrial equipment, particularly for identifying faults and recommending corrective actions. The system includes a diagnosis/prognosis module that analyzes equipment data to detect faults and predict potential failures. A key feature is the use of plug-in processing modules, which are modular components that extend the system's functionality. One such module is a remedial action recommendation module, which processes fault objects generated by the diagnosis/prognosis module. The remedial action recommendation module generates remedial action objects that provide detailed guidance for addressing identified issues. Each remedial action object includes a description of the required action, a priority level and timeframe for execution, a traceable explanation linking data insights to the root cause, and domain-specific properties for integration with downstream improvement systems. This modular approach allows for flexible customization and scalability, enabling the system to adapt to different industrial applications and integrate with other maintenance or optimization systems. The invention improves equipment reliability by providing actionable insights and structured recommendations for fault resolution.
13. The method of claim 12 comprising converting the input data to a plurality of unified data objects and making the unified data objects accessible to the plurality of plug-in processing modules and the at least one downstream improvement system via the enterprise service bus.
This invention relates to data processing systems that integrate multiple plug-in processing modules and downstream improvement systems. The problem addressed is the difficulty of efficiently sharing and processing diverse input data across different modules and systems within an enterprise architecture. The solution involves converting input data into a standardized format, represented as unified data objects, which can be universally accessed and utilized by various processing modules and improvement systems. These unified data objects are made available through an enterprise service bus, acting as a central communication backbone. The enterprise service bus facilitates seamless data exchange, ensuring that the unified data objects are accessible to all connected components. This approach enables modular, scalable, and interoperable data processing, allowing different systems to leverage the same standardized data without requiring custom integration for each module or system. The invention enhances data consistency, reduces integration complexity, and supports dynamic system expansion by maintaining a unified data structure across the enterprise architecture.
14. The method of claim 13 comprising filtering the input data prior to converting the input data to unified data objects.
A system and method for processing input data involves converting diverse input data into unified data objects for analysis. The input data may originate from various sources and formats, such as structured databases, unstructured text, or sensor readings, which can complicate integration and analysis. The method addresses this by transforming the input data into a standardized format, enabling consistent processing and comparison. Before conversion, the input data undergoes filtering to remove irrelevant or noisy data, ensuring higher-quality unified data objects. This filtering step may include removing duplicates, correcting errors, or applying domain-specific rules to refine the input data. The unified data objects are then generated, allowing for efficient storage, retrieval, and analysis across different applications. The method supports real-time or batch processing, depending on the use case, and can be applied in fields such as data integration, machine learning, or business intelligence. By standardizing input data and pre-filtering it, the system improves data consistency and reduces processing overhead.
15. The method of claim 12 comprising aggregating the output data from the plurality of plug-in processing modules, and implementing a conflict resolution process if a conflict is detected in the aggregate output data.
This invention relates to data processing systems that use multiple plug-in processing modules to analyze or transform input data. The problem addressed is ensuring consistent and conflict-free output when multiple modules generate overlapping or contradictory results. The system includes a core processing engine that receives input data and distributes it to a plurality of plug-in processing modules. Each module performs a specific processing task, such as data analysis, transformation, or enrichment, and generates output data. The core engine then aggregates the output data from all modules. If conflicts are detected in the aggregated results—for example, when different modules provide conflicting values for the same data field—the system implements a conflict resolution process. This process may involve prioritizing certain modules, applying predefined rules, or using statistical methods to determine the most accurate or relevant output. The invention ensures that the final aggregated data is coherent and reliable, even when multiple modules contribute to the processing pipeline. The conflict resolution mechanism can be customized based on the specific requirements of the application, such as prioritizing real-time data over historical data or favoring modules with higher accuracy metrics. This approach enhances the robustness and reliability of data processing systems that rely on modular, plug-in architectures.
16. The method of claim 15 comprising receiving baseline reference data, the baseline reference data comprising baseline intelligence and/or configuration data, and making the baseline reference data available to the plurality of plug-in processing modules via the enterprise service bus.
This invention relates to a system for managing and processing data within an enterprise environment using a modular architecture. The system addresses the challenge of integrating diverse data sources and processing modules in a scalable and flexible manner. The core of the system is an enterprise service bus (ESB) that facilitates communication between various components, including plug-in processing modules and data sources. These plug-in modules are designed to handle specific tasks such as data transformation, analysis, or integration, and can be dynamically added or removed from the system without disrupting overall operations. The system includes a mechanism for receiving baseline reference data, which encompasses intelligence and configuration data. This baseline data serves as a foundational reference for the plug-in modules, ensuring consistency and accuracy in their operations. The ESB makes this baseline data accessible to all plug-in modules, enabling them to leverage it for their respective functions. The modular design allows for easy customization and expansion, as new processing modules can be integrated seamlessly into the system. This approach enhances the system's adaptability to changing business needs and technological advancements. The overall architecture promotes efficient data processing, improved interoperability, and reduced complexity in managing enterprise-wide data workflows.
17. The method of claim 16 comprising adjusting the baseline reference data to reduce the likelihood of conflict in output data from the plurality of plug-in processing modules.
This invention relates to data processing systems that use multiple plug-in processing modules to generate output data. The problem addressed is ensuring consistency and reducing conflicts in the output data when these modules operate independently. The method involves adjusting baseline reference data to minimize discrepancies between the outputs of different modules. The baseline reference data serves as a common foundation for the modules, and adjustments are made to align their processing results. This includes dynamically modifying the reference data based on feedback from the modules to prevent conflicting outputs. The system may also prioritize certain modules or data sources to resolve conflicts when they arise. The method ensures that the combined output data from all modules remains coherent and reliable, even when the modules have different processing algorithms or data interpretations. This approach is particularly useful in systems where multiple independent modules contribute to a unified output, such as in data analysis, machine learning, or distributed computing environments. The adjustments to the baseline reference data can be automated or manually controlled, depending on the system requirements. The goal is to maintain consistency across the outputs while allowing the modules to operate with their respective processing logic.
18. The method of claim 15 comprising aggregating output data into hierarchies to produce higher level outputs, such as by roll-up of condition indicators across multiple asset components.
This invention relates to data aggregation and hierarchical analysis in asset monitoring systems. The technology addresses the challenge of managing and interpreting large volumes of condition data from multiple asset components, such as in industrial or infrastructure systems, to derive meaningful insights. The method involves collecting condition indicators from individual asset components, which may include sensors or diagnostic tools that monitor performance, health, or operational status. These indicators are then processed to generate output data that reflects the condition of each component. The key innovation is the aggregation of this output data into hierarchical structures, allowing for the roll-up of condition indicators across multiple components. This hierarchical aggregation enables higher-level outputs that provide a consolidated view of asset health, facilitating trend analysis, predictive maintenance, and decision-making. The method may also include filtering or weighting of condition indicators to prioritize relevant data before aggregation. By organizing data in this way, the system enhances situational awareness and reduces the complexity of monitoring large-scale assets. The approach is particularly useful in environments where assets consist of interconnected components, such as machinery, power grids, or transportation networks, where understanding system-wide performance is critical.
19. The method of claim 12 comprising generating a notification based at least in part on output data objects that are pushed onto the enterprise service bus which satisfy certain conditions.
This invention relates to enterprise service bus (ESB) systems, specifically methods for monitoring and processing data objects transmitted through the bus. The problem addressed is the need to efficiently detect and respond to specific data events within an ESB environment, where large volumes of data objects are exchanged between distributed systems. The solution involves generating notifications when data objects pushed onto the ESB meet predefined conditions, enabling real-time event-driven actions. The method includes monitoring data objects as they are transmitted through the ESB. These objects are evaluated against a set of conditions, which may include criteria such as data content, metadata, or timing attributes. When an object satisfies the conditions, a notification is generated. This notification can trigger downstream processes, alert systems, or log events for further analysis. The conditions may be dynamically configurable, allowing the system to adapt to changing operational requirements. The approach ensures that critical data events are promptly identified and acted upon, improving system responsiveness and operational efficiency. By leveraging the ESB as a central communication hub, the method provides a scalable and unified way to monitor and react to data flows across enterprise systems. This is particularly useful in environments where timely detection of specific data patterns or anomalies is essential for business processes or system integrity.
20. The method of claim 12 wherein the plurality of plug-in processing modules comprises a data shaping module configured to process raw data and generate condition and key performance indicators based on the raw data, wherein each of the condition and key performance indicators comprises one or more of: a metric of a condition of an asset or task process, attributes such as source data, processing descriptions and statistics, and audit information that can be used by the next step to assist in its subsequent use and interrogation.
This invention relates to a system for processing and analyzing raw data to generate condition and key performance indicators (KPIs) for monitoring assets or task processes. The system includes a plurality of plug-in processing modules, one of which is a data shaping module. The data shaping module processes raw data to extract and generate condition and KPIs. These indicators include metrics that reflect the condition of an asset or task process, along with attributes such as source data, processing descriptions, statistics, and audit information. The generated indicators are structured to be used in subsequent processing steps, enabling further analysis, interrogation, and decision-making. The system allows for modular and flexible data processing, where different processing modules can be integrated to handle various aspects of data analysis. The data shaping module ensures that the processed data is enriched with relevant metadata and contextual information, making it more useful for downstream applications. This approach improves data usability and facilitates more accurate and informed monitoring of assets and processes.
21. The method of claim 20 wherein the plurality of plug-in processing modules comprises a diagnosis/prognosis module configured to receive condition and key performance indicators generated by the data shaping module, and based at least in part on the condition and key performance indicators, generate fault objects, each of the fault objects comprising one or more of: a description or identification of the likely fault, confidence factor, weighted associated root cause, residual life estimate, meta-data about the method and explanation of how the conclusion was reached and a trace back to the underlying condition indications.
This invention relates to predictive maintenance systems for industrial equipment, addressing the challenge of accurately diagnosing and predicting faults before they cause failures. The system uses a modular architecture with plug-in processing modules to analyze equipment data. One key module is a diagnosis/prognosis module that receives condition and key performance indicators from a data shaping module. The data shaping module processes raw sensor data into structured condition indicators and performance metrics. The diagnosis/prognosis module then evaluates these inputs to generate fault objects, which provide detailed fault information. Each fault object includes a description or identification of the likely fault, a confidence factor indicating the likelihood of the fault, a weighted root cause analysis, an estimate of the equipment's remaining useful life, metadata about the diagnostic process, and an explanation of how the conclusion was reached. Additionally, the fault object includes a trace back to the underlying condition indicators that contributed to the diagnosis, ensuring transparency and traceability. This approach enhances maintenance decision-making by providing actionable insights with clear evidence and reasoning.
22. The method of claim 21 wherein the plurality of plug-in processing modules comprises a remedial action recommendation module configured to receive fault objects generated by the diagnosis/prognosis module, and based at least in part on the fault objects, generate remedial action objects, each of the remedial action objects comprising one or more of: a description or identification of the action that should to be taken to solve or prevent an issue, a priority and time-window within which the action should take place, an explanation tracing from data to condition insights to fault and root case, and domain specific properties for use by the at least one downstream improvement system.
This invention relates to a diagnostic and prognostic system for industrial equipment, focusing on automated fault detection, analysis, and remedial action generation. The system addresses the challenge of efficiently identifying and resolving equipment faults while minimizing downtime and operational disruptions. A key component is a remedial action recommendation module that processes fault objects generated by a diagnosis/prognosis module. These fault objects represent detected issues in the equipment. The remedial action recommendation module generates remedial action objects, which include detailed instructions for resolving or preventing the identified faults. Each remedial action object contains a description or identification of the required action, a priority level, and a time-window for execution. Additionally, it provides an explanation tracing the data analysis steps from raw data to condition insights, fault identification, and root cause determination. The remedial action objects also include domain-specific properties that can be utilized by downstream improvement systems, such as maintenance scheduling or predictive analytics platforms. This modular approach ensures that the system can adapt to various industrial applications while providing actionable insights for maintenance and operational improvements.
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November 22, 2019
May 28, 2024
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